Imperial College London
Portrait Picture

Professor Omar K. Matar, FREng

Faculty of Engineering, Department of Chemical Engineering

Head of Department of Chemical Engineering
 
 
 
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Contact

 

+44 (0)20 7594 9618o.matar Website

 
 
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Assistant

 

Mr Avery Kitchens +44 (0)20 7594 6263

 
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Location

 

305 ACEACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Inguva:2020:10.1017/dce.2020.14,
author = {Inguva, P and Mason, LR and Pan, I and Hengardi, M and Matar, OK},
doi = {10.1017/dce.2020.14},
journal = {Data-Centric Engineering},
title = {Numerical simulation, clustering, and prediction of multicomponent polymer precipitation},
url = {http://dx.doi.org/10.1017/dce.2020.14},
volume = {1},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:title>Abstract</jats:title>  <jats:p>Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.</jats:p>
AU  - Inguva,P
AU  - Mason,LR
AU  - Pan,I
AU  - Hengardi,M
AU  - Matar,OK
DO  - 10.1017/dce.2020.14
PY  - 2020///
TI  - Numerical simulation, clustering, and prediction of multicomponent polymer precipitation
T2  - Data-Centric Engineering
UR  - http://dx.doi.org/10.1017/dce.2020.14
VL  - 1
ER  -
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